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Applications of Belief Functions in Business Decisions: A Review

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Abstract

In this paper, we review recent applications of the Dempster-Shafer theory (DST) of belief functions to auditing and business decision-making. We show how DST can better map uncertainties in application domains than Bayesian theory of probabilities. We review the applications in auditing around three practical problems that challenge the effective application of DST, namely, hierarchical evidence, versatile evidence, and statistical evidence. We review the applications in other business decisions in two loose categories: judgment under ambiguity and business model combination. Finally, we show how the theory of linear belief functions, a new extension to DST, can provide an alternative solution to a wide range of business problems.

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Authors and Affiliations

  1. Ernst & Young Center for Auditing Research and Advanced Technology, Division of Accounting and Information System, School of Business, The University of Kansas, Lawrence, Kansas, 66045, USA

    Rajendra P. Srivastava (Ernst & Young Professor of Accounting and Director)

  2. College of Business Administration, University of Akron, 351, Akron, OH, 44325-4801, USA

    Liping Liu (Associate Professor of Management and Information Systems)

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  1. Rajendra P. Srivastava
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Srivastava, R.P., Liu, L. Applications of Belief Functions in Business Decisions: A Review. Information Systems Frontiers 5, 359–378 (2003). https://doi.org/10.1023/B:ISFI.0000005651.93751.4b

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